Numerous modern advanced driver assistance systems (ADAS), and in particular highly automated vehicle systems for urban automated driving (UAD), require sufficiently accurate vehicle localization or location. Localization systems that use a global localization map (e.g. retrievable from a back-end server), and a local localization map from an environmental model of the vehicle system, are often utilized in this context.
Localization approaches, for example described in C. Heigele, H. Mielenz, J. Heckel, D. Schramm, “Accurate and fast localization in unstructured environment . . . based on shape context keypoints,” in Information Fusion (FUSION), 2014 17th International Conference, Jul. 7-10, 2014, 1-7, use localization maps, and use map-matching algorithms that are optimized for the intended application, to determine a map-relative vehicle posture (position and orientation of the vehicle).
In most cases, maximally computation-efficient algorithms having a deterministic runtime behavior are preferred. For landmark-based localization relative to a global localization map, for example, the closed solution to the so-called “Procrustes problem” can be used in order to calculate the transformation between a local and a global localization map, as described, e.g., in J. Rohde, J. E. Stellet, H. Mielenz, J. M. Zollner, “Model-Based Derivation of Perception Accuracy Requirements for Vehicle Localization in Urban Environments,” in Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference, Sep. 18, 2015, pages 712-718.
These methods cannot be applied in all possible circumstances, however, and it is therefore necessary to resort, for example, to less efficient algorithms. Dynamic effects, for example changing occupancy of parking spaces next to the roadway, as can occur especially in an urban environment (e.g., if the global localization map differs greatly from the local localization map), or cases in which map-relative localization is entirely impossible (e.g., in traffic jam situations), require particular map-matching approaches.
In the latter case, the entire simultaneous localization and mapping (SLAM) problem must be solved in order to make localization possible in the first place. For this, substantially simultaneous creation of a map, and localization based on the created map, are carried out. With this approach, the segment to be traveled is limited by drift in the posture estimate, and for that reason map-relative localization exhibits advantageous properties.
An intelligent system for road illumination was presented by Continental AG at the IST conference in 2016. The system presented there possesses a variety of environmental sensors and transmits information, for example regarding defective lamps, to a central data server. In a further expansion phase, a unit for communication with vehicle systems is also provided.
D. Meyer-Delius, J. Hess, G. Grisetti, W. Burgard, “Temporary maps for robust localization in semi-static environments,” in Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference, Oct. 18-22, 2010, pp. 5750-5722 discloses a system that performs a switchover between a conventional map-relative localization approach and a SLAM approach. A semi-static environment is detected, and a switchover between the two approaches recited above is carried out.
A plurality of approaches for sensor distribution planning (e.g., for monitoring tasks) are also described, for example, in Y. Zou, Krishnendu Chakrabarty, “Sensor deployment and target localization based on virtual forces,” in INFOCOM 2003, Twenty-Second Annual Joint Conference of the IEEE Computer and Communications-IEEE Societies, Vol. 2, pp. 1293-1303, Vol. 2, 2003.
Japan Patent Application No. JP 2003524775 A describes a navigation device for a vehicle in which an adaptation of sensor data to digital map data corresponding to a current position of the vehicle can be carried out.
PCT Application No. WO 2015/156821 A1 describes a vehicle localization system having a first localization system, a second localization system, and a control device. The first localization system is embodied to localize the vehicle using first data; the second localization system is embodied to localize the vehicle using second data. The control device is embodied to switch over between the first and the second localization system if the first data are less than a predefined extent.